用深度学习自动识别并翻译古埃及象形文字，准确率显著提升。
HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English
- 分三步：图像分割、符号映射到戈丁分类码、中文转英文
- 在两个数据集上实现42.2的BLEU得分，优于以往方法
- 适合对古代文字数字化和历史文献自动化处理的研究者
古埃及象形文字是一种完全由图画构成的古老书写系统。将其翻译成英文面临诸多挑战，例如单个象形文字可能具有多种含义。随着深度学习翻译技术的快速发展，已产生显著影响。本研究提出一种从图像自动识别并翻译古埃及象形文字至英文的方法。研究使用了两个数据集：Morris Franken数据集和EgyptianTranslation数据集。该方法分为三个阶段：图像分割（采用轮廓法与Detectron2）、将符号映射到戈丁代码（Gardiner codes），以及翻译（使用CNN模型）。最终模型在翻译任务中获得42.2的BLEU分数，相比先前研究有显著提升。
原文摘要 · Abstract (English)
Egyptian hieroglyphs, the ancient Egyptian writing system, are composed entirely of drawings. Translating these glyphs into English poses various challenges, including the fact that a single glyph can have multiple meanings. Deep learning translation applications are evolving rapidly, producing remarkable results that significantly impact our lives. In this research, we propose a method for the automatic recognition and translation of ancient Egyptian hieroglyphs from images to English. This study utilized two datasets for classification and translation: the Morris Franken dataset and the EgyptianTranslation dataset. Our approach is divided into three stages: segmentation (using Contour and Detectron2), mapping symbols to Gardiner codes, and translation (using the CNN model). The model achieved a BLEU score of 42.2, a significant result compared to previous research.
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